(1) Technical Field
The present invention relates to techniques for fusing sensor data for object tracking. More specifically, the present invention relates to a method, apparatus and computer program product that uses both kinematic and feature-based tracking using probabilistic argumentation.
(2) Discussion
Multiple Target Tracking (MTT) is an essential requirement for surveillance, tracking, and control systems that employ one or more sensors to interpret an environment that includes both true objects and false alarms. The goal of tracking is to robustly provide accurate and timely information regarding the number of objects and their state and type. This is important in both military and commercial applications. A typical military application is automated target tracking and identification of ground and aerial targets. An example of a commercial application is in automotive forward collision warning system, which requires both kinematic and type (roadside/vehicle/pedestrian) information on in-path obstacles for effective system performance. Another commercial application is automatic tracking of aircraft in an air show, and identification of ground and aerial targets. The central problem of multiple target tracking is that of associating sensor measurements with existing or new tracks. This data association process is uncertain due to many factors, non-limiting examples of which include inaccurate measurements, partially obscured targets, closely-spaced target configurations, poor signal-to-noise ratio, random false alarms in the detection process, clutter near the target of interests, interfering targets, and decoys or other countermeasures.
The current state-of-the-art in MTT is a Kalman filter-based multiple hypothesis tracker (MHT) that uses kinematic (position, velocity, etc.) information to carry out the association process. A multiple hypothesis tracking (MHT) data association method with interacting multiple model (IMM) filtering is described in the publication “Design and Analysis of Modem Tracking Systems,” cited herein, and has been applied to several real-world applications. This tracker uses kinematic (or metric) quantities and certain signal-related quantities for data association. No use is made of features in data association. Both the memory and computation requirements of MHT increase exponentially with problem size. In most applications, the target identification or class information is obtained by using feature measurements outside of a kinematic data association module. While this approach works well for relatively benign environments, it performs poorly in difficult environments. For example, it defaults to group tracking in dense track environments, or loses track during complex movements or over long periods of time.
The need for accurate and timely information on the kinematic state and features of an object is important for both military and commercial applications. In most cases, the kinematic information (position/velocity/acceleration) and features (class/identification (ID)/type) are obtained using sequential processing (where one type of information aids the estimation of the other) or through completely uncoupled and independent processing. This could result in poor track and feature estimation, lost tracks, misidentified objects, slow system response, etc. The present invention will overcome these limitations. It will increase the robustness of the data association operation, which is the most difficult and error-prone operation in multi-target tracking. As a result of robust data association, the target identity estimation will be more accurate.
Previous methods that combine features and kinematic information suffer from the limitation that they use Bayesian methods, and thus require complete knowledge of prior probabilities and a full probability description of features for all target classes, which are often unavailable. The technique of the present invention will overcome this limitation. Since domain knowledge about target classes and their features is often most naturally expressed in the form of logical statements or rules, the technique of the present invention would be more suitable, allowing the knowledge base to be logic rules and permitting a much more general expression of ignorance and uncertainty. As a result, systems of the present invention would be much more flexible and applicable to a large domain of problem areas and applications than are those of the prior art.
Major improvements to the state-of-the-art are needed, but can only be achieved by a paradigm shift in approaches to the MTT problem. The present invention, by using a combination of joint kinematic and feature tracking including probabilistic argumentation, helps to overcome the aforementioned limitations.
For information regarding the previous systems and methods, as well as background information, the reader is directed to the following references.
[1] S. Blackman and R. Popoli, Design and Analysis of Modem Tracking Systems, Artech House, 1999.
[2] R. W. Sittler, “An Optimal Data Association Problem in Surveillance Theory,” IEEE Trans. On Military Electronics, Vol. MIL-8, April 1964, pp. 125–139.
[3] E. Bosse and J. Roy, “Fusion of Identity Declarations from Dissimilar Sources using Dempster-Shafer Theory”, Optical Eng., Vol. 36, No. 3, March 1997, pp. 648–657.
[4] E. Blasch and L. Hong, “Data association through fusion of target track and Identification Sets, 3rd International Conference on Data Fusion, Fusion 2000, July 2000.
[5] K. J. Sullivan, M. B. Ressler, R. L. Williams, “Signature-aided tracking using HRR profiles”, Proc. SPIE Conf. on algorithms for synthetic aperture Radar Imagery VIII, vol. 4382, p. 132–142, 2001.
[6] J. Kohlas and R. Haenni, “Assumption-Based Reasoning and Probabilistic Argumentation Systems”, Tech, report 96-07. Institute of Informatics, University of Fribourg. 1996.
[7] B. Anrig, R. Bissig, R. Haenni, J. Kohlas, and N. Lehmann, “Probabilistic Argumentation Systems: Introduction to Assumption-Based Modeling with ABEL”, Institute of Informatics, University of Fribourg. 1998.
[8] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Network of Plausible Inference, Morgan Kaufmann, 1988.
[9] S. Blackman, Multiple-Target tracking with Radar Applications, Artech House, 1986.